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Comprehensive learning particle swarm optimization for sizing and placement of distributed generation for network loss reduction

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dc.citedby8
dc.contributor.authorKarunarathne E.en_US
dc.contributor.authorPasupuleti J.en_US
dc.contributor.authorEkanayake J.en_US
dc.contributor.authorAlmeida D.en_US
dc.contributor.authorid57216633155en_US
dc.contributor.authorid11340187300en_US
dc.contributor.authorid7003409510en_US
dc.contributor.authorid57211718103en_US
dc.date.accessioned2023-05-29T08:07:24Z
dc.date.available2023-05-29T08:07:24Z
dc.date.issued2020
dc.description.abstractWith the technological advancements, distributed generation (DG) has become a common method of overwhelming the issues like power losses and voltage drops which accompanies with the leaf of the feeders of radial distribution networks. Many researchers have used several optimization techniques and tools which could be used to locate and size the DG units in the system. Particle swarm optimization (PSO) is one of the famous optimization techniques. However, the premature convergence is identified as a fundamental adverse effect of this optimization technique. Therefore, the optimization problem can direct the objective function to a local minimum. This paper presents a variant of PSO techniques, "comprehensive learning particle swarm optimization (CLPSO)"to determine the optimal placement and sizing of the DGs, which uses a novel learning strategy whereby all other particles' historical best information and learning probability value are used to update a particle's velocity. The CLPSO particles learn from one exampler for few iterations, instead of learing from global and personal best values in every iteration in PSO and this technique retains the swarm's variability to avoid premature convergence. A detailed analysis was conducted for the IEEE 33 bus system. The comparison results have revealed a higher convergence and an accuracy than the PSO. Copyright � 2020 Institute of Advanced Engineering and Science. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.11591/ijeecs.v20.i1.pp16-23
dc.identifier.epage23
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85088249266
dc.identifier.spage16
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85088249266&doi=10.11591%2fijeecs.v20.i1.pp16-23&partnerID=40&md5=67fd157e7b8b11157d27d6c951752e3b
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25216
dc.identifier.volume20
dc.publisherInstitute of Advanced Engineering and Scienceen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleIndonesian Journal of Electrical Engineering and Computer Science
dc.titleComprehensive learning particle swarm optimization for sizing and placement of distributed generation for network loss reductionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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